Tang Minli, Wu Longxin, Yu Xinyu, Chu Zhaoqi, Jin Shuting, Liu Juan
Department of Computer Science and Technology, Xiamen University, Xiamen, China.
School of Big Data Engineering, Kaili University, Kaili, China.
Front Genet. 2021 Nov 22;12:784863. doi: 10.3389/fgene.2021.784863. eCollection 2021.
Proteins are the basic substances that undertake human life activities, and they often perform their biological functions through interactions with other biological macromolecules, such as cell transmission and signal transduction. Predicting the interaction sites between proteins can deepen the understanding of the principle of protein interactions, but traditional experimental methods are time-consuming and labor-intensive. In this study, a new hierarchical attention network structure, named HANPPIS, by adding six effective features of protein sequence, position-specific scoring matrix (PSSM), secondary structure, pre-training vector, hydrophilic, and amino acid position, is proposed to predict protein-protein interaction (PPI) sites. The experiment proved that our model has obtained very effective results, which was better than the existing advanced calculation methods. More importantly, we used the double-layer attention mechanism to improve the interpretability of the model and to a certain extent solved the problem of the "black box" of deep neural networks, which can be used as a reference for location positioning on the biological level.
蛋白质是承担人类生命活动的基本物质，它们常常通过与其他生物大分子相互作用来履行其生物学功能，如细胞传递和信号转导。预测蛋白质之间的相互作用位点可以加深对蛋白质相互作用原理的理解，但传统的实验方法既耗时又费力。在本研究中，通过添加蛋白质序列、位置特异性得分矩阵（PSSM）、二级结构、预训练向量、亲水性和氨基酸位置这六个有效特征，提出了一种名为HANPPIS的新型分层注意力网络结构，用于预测蛋白质-蛋白质相互作用（PPI）位点。实验证明，我们的模型取得了非常有效的结果，优于现有的先进计算方法。更重要的是，我们使用双层注意力机制提高了模型的可解释性，并在一定程度上解决了深度神经网络的“黑箱”问题，可为生物水平上的定位提供参考。
